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Laser & Optoelectronics Progress, Volume. 61, Issue 9, 0901004(2024)

Ocean-Land Waveform Classification Based on Multichannel Weighted Voting of Airborne Green Laser

Xinglei Zhao1、*, Gang Liang1, Jianhu Zhao2, and Fengnian Zhou3
Author Affiliations
  • 1College of Information Science and Engineering, Shandong Agricultural University, Tai'an 271018, Shandong, China
  • 2School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, Hubei, China
  • 3The Survey Bureau of Hydrology and Water Resources of Yangtze Estuary, Shanghai 200136, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (9)
    Equations (11)
    References (22)
    Cited By (2)
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    Figures & Tables(9)
    Structure of proposed MWV-CNN

    Fig. 1. Structure of proposed MWV-CNN

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    Distributions of multichannel field of views of Optech CZMIL system (non-scaled)

    Fig. 2. Distributions of multichannel field of views of Optech CZMIL system (non-scaled)

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    Structure of 1D CNN module

    Fig. 3. Structure of 1D CNN module

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    Typical laser waveforms of ocean. (a) IR laser channel; (b) green laser deep channel; (c)‒(i) green laser shallow 0‒6 channels

    Fig. 4. Typical laser waveforms of ocean. (a) IR laser channel; (b) green laser deep channel; (c)‒(i) green laser shallow 0‒6 channels

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    Typical laser waveforms of land. (a) IR laser channel; (b) green laser deep channel; (c)‒(i) green laser shallow 0‒6 channels

    Fig. 5. Typical laser waveforms of land. (a) IR laser channel; (b) green laser deep channel; (c)‒(i) green laser shallow 0‒6 channels

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    Training process of 1D CNN in MWV-CNN

    Fig. 6. Training process of 1D CNN in MWV-CNN

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    Classification results of different ocean-land waveform classification methods

    Fig. 7. Classification results of different ocean-land waveform classification methods

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    Relationship between classification accuracy and channel number of MWV-CNN and MV-CNN

    Fig. 8. Relationship between classification accuracy and channel number of MWV-CNN and MV-CNN

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    • Table 1. Confusion matrices and performance metrics of different ocean-land waveform classification methods

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      Table 1. Confusion matrices and performance metrics of different ocean-land waveform classification methods

      MethodClassPredictionOA /%KappaSDOA /%
      OceanLand
      SVMOcean24046167298.520.949—
      Land369949822
      1D CNNOcean237975315898.700.9570.48
      Land68452837
      MV-CNNOcean24030982499.420.9800.03
      Land89852623
      MWV-CNNOcean24036676799.450.9820.02
      Land84952672
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    Xinglei Zhao, Gang Liang, Jianhu Zhao, Fengnian Zhou. Ocean-Land Waveform Classification Based on Multichannel Weighted Voting of Airborne Green Laser[J]. Laser & Optoelectronics Progress, 2024, 61(9): 0901004

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    Paper Information

    Category: Atmospheric Optics and Oceanic Optics

    Received: Dec. 5, 2022

    Accepted: Dec. 23, 2022

    Published Online: May. 6, 2024

    The Author Email: Xinglei Zhao (xingleizhao@126.com)

    DOI:10.3788/LOP223239

    CSTR:32186.14.LOP223239

    Topics

    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology